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IBM Watson Data Scientist v1 certification is designed for professionals who are looking to validate their skills in the field of data science. IBM Watson Data Scientist v1 certification exam is aimed at individuals who have a solid understanding of data science concepts and techniques, as well as experience working with IBM Watson. By passing C1000-154 exam, candidates can demonstrate their expertise in data science and their ability to work with IBM Watson technologies.
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IBM C1000-154 exam is designed to test the knowledge and skills of data scientists who work with IBM Watson. IBM Watson Data Scientist v1 certification is aimed at professionals who want to demonstrate their proficiency in using Watson to analyze large sets of data and gain insights into complex business problems. C1000-154 exam covers a wide range of topics related to data science, including data exploration, data preparation, statistical analysis, machine learning, and data visualization.
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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Deploy the Solution | 10% | - Monitor model performance post-deployment - Deploy models as APIs in Watson - Ensure scalability and reliability |
| Prepare the Data | 18% | - Use Watson tools for data preparation - Feature engineering and selection - Clean, transform, and normalize datasets - Handle missing values and outliers |
| Understand the Business Problem | 12% | - Define success metrics and constraints - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives |
| Evaluate the Model | 15% | - Assess classification/regression metrics - Identify bias and overfitting - Validate model generalizability |
| Visualization and Storytelling | 5% | - Create effective visualizations - Communicate results to stakeholders |
| Collect and Explore the Data | 15% | - Identify and access data sources in Watson Studio - Detect patterns, outliers, and correlations - Perform descriptive statistics and exploratory analysis |
| Build the Model | 20% | - Compare and select best performing models - Train models using Watson AutoAI and SPSS - Perform hyperparameter tuning - Select appropriate ML algorithms |
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